Can quantitative parameters of spectral computed tomography predict lymphatic metastasis in lung cancer? a systematic review and meta-analysis

Wang, Tong; Fan, Zheng; Zou, Lue; Hou, Yang

doi:10.1016/j.radonc.2023.109643

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Recent studies have focused on λHu for detecting LN metastasis in patients with malignant tumors. A meta-analysis by Wang et al [30] evaluated the utility of quantitative spectral CT parameters in identifying LN metastasis in lung cancer. Incorporating 11 studies with 1290 cases, the analysis revealed that the diagnostic performance of NIC and λHu in identifying lymphatic metastasis surpassed that of short-axis diameter, with a combined AUC exceeding 0.8.…”

Section: Spectral Hounsfield Unit Attenuation Curvesmentioning

confidence: 99%

Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review

Chen,

Jiang,

Zhou

et al. 2024

Diagnostics

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The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.

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Section: Spectral Hounsfield Unit Attenuation Curvesmentioning

confidence: 99%